53 resultados para Module MAPK
Resumo:
devcon transforms the coefficients of 0/1 dummy variables so that they reflect deviations from the "grand mean" rather than deviations from the reference category (the transformed coefficients are equivalent to those obtained by the so called "effects coding") and adds the coefficient for the reference category. The variance-covariance matrix of the estimates is transformed accordingly. The transformed estimated can be used with post estimation procedures. In particular, devcon can be used to solve the identification problem for dummy variable effects in the so-called Blinder-Oaxaca decomposition (see the oaxaca package).
Resumo:
-cochran- performs a test for equality of two or more proportions in matched samples. The chi-squared calculated by -cochran- is known as Cochran's Q (Cochran 1950)
Resumo:
duncan computes the Duncan and Duncan segregation statistic (dissimilarity index D) from individual level data.
Resumo:
estout produces a table of regression results from one or several models for use with spreadsheets, LaTeX, HTML, or a word-processor table. eststo stores a quick copy of the active estimation results for later tabulation. esttab is a wrapper for estout. It displays a pretty looking publication-style regression table without much typing. estadd adds additional results to the e()-returns for one or several models previously fitted and stored. This package subsumes the previously circulated esto, esta, estadd, and estadd_plus. An earlier version of estout is available as estout1.
Resumo:
mrtab tabulates multiple responses which are held as a set of indicator variables or as a set of polytomous response variables.
Resumo:
wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.
Resumo:
alphawgt computes the Cronbach's alpha statistic. It is the same than the official alpha (version 4.5.2, 09apr2002) except that fweights and aweights may be applied.
Resumo:
Given the results from two regressions (one for each of two groups), decompose computes several decompositions of the outcome variable differential. The decompositions shows how much of the gap is due to differing endowments between the two groups, and how much is due to discrimination. Usually this is applied to wage differentials using Mincer type earnings equations.